Automatic Number Plate Character Recognition using Paddle-OCR

With increasing traffic on roads today, advanced technology is in great demand in order to monitor and manage traffic. Artificially intelligent systems are in demand for automated vehicle identification and number plate recognition. Automated number plate recognition (ANPR) uses machine learning and image processing techniques to perform tasks like counting vehicles, database management and parking violation alerts. They provide a lot of scope to abstain from human intervention since they provide real-time data on vehicle ingress and egress. This technology is a huge asset when it comes to traffic management, eliminating manpower requirements to a large extent. This paper proposes an image processing-based ANPR system using Paddle OCR. The system aims to automatically recognize unique number plates of vehicles, enabling intelligent traffic and vehicle management. The primary stages of this process is image capture, vehicle plate identification, the detection of edges, division of characters, and identification of characters in the number plates. PaddleOCR is one of the latest models for optical character recognition, hence it achieves a lot of efficiency in real-world scenarios.